Abstract

The bubble detector is used to detect the amount of neutron and hence used to measure the neutron dose under intense gamma field. Usually, this device is used in the field of atomic energy to manipulate the neutron dosage. The radioactive premises bubbles will be formed based on the radioactive. It can be used to monitor radioactive exposure. The number of nucleated bubbles yields the neutron dose. Hence the accuracy of the measurement depends on the counting of bubbles. This work proposes an image processing based different edge detection technique to find the edges of the bubbles and analyses the edge detection operator. Whereas several algorithms are projected for the aim of image edge detection technique, the matter of finding the image edge detection remains an open challenge in the image processing field, particularly in things wherever the image quality is poor condition. In this paper we deal with the finding and extraction of bubble edge of a Dosimeter bubble detector image using different digital image processing edge detection techniques. This technique is used for finding discontinuities in intensity values. Thereby it is used for finding the boundaries of bubble objects within the image. Here in this study, we can come to know the boundaries of bubbles which can be found in different edge detection technique to get better results for counting bubbles and various advantages and disadvantages of edge detection techniques.

Keywords

Introduction

Bubble detector dosimeter is used to measure the neutron
dosage in the premises. Removing noises from the image may
be a difficult task for researchers even until date. There are
numerous approaches and techniques that have its own
limitations and benefits.

The study based on the concept of edge detection after denoising
of digital image before getting data from an image,
removal of noises from the image is necessary and thus
becomes the first step in image processing [1]. Most of the
natural images are assumed to have additive random noise
which is modeled as a Gaussian. Thus this paper helps us to
know how to apply different edge detection techniques and
analyses its result and output images.

Edge Detection

Edges accommodate important features and contain vital data.
It considerably reduces the image size and filters out data that
will be considered less relevant, therefore conserving the vital structural properties of an image. Most images contain some
quantity of redundancies that may generally be removed once
edges are detected and replaced throughout the reconstruction.
Thus here the edge detection comes into play. Also, edge
detection is one among the ways of constructing images that
does not take up an excessive amount of space within the
hardware. Here in this paper, we will discuss the four edge
detection techniques namely:

1. Sobel edge detection operator.

2. Roberts cross operator.

3. Prewitt operator.

4. Canny operator.

An overview of edge detection techniques and comparative
study of edge detectors on the dental radiographs and
application of edge detectors such as Prewitt, Canny, and Sobel
[2]. There are different ways to perform edge detection, which
is mainly categorized into two: Gradient and Laplacian. The
gradient method finds the edges by calculating the maximum and minimum in the first derivative of the image. Whereas in
the Laplacian method, it searches for zero crossing in the
second derivative of the image to find the edges. There are
disadvantages of using gradient-based edge detection methods
in X-ray images [3].

Edge Detection Techniques

Sobel operator

The Sobel edge detector uses two masks, one is vertical and
another one is horizontal. These masks use 3 × 3 matrices.
Sobel has two main advantages: the random noise of the
images are met with smoothing effects and since it is the
differential of two rows or two columns, the element on the
edge found on both the sides gets enhanced, thus the edge will
be thick and bright [4]. This edge detection technique extracts
all of the edges, regardless of direction. Thus the resultant
image appears as a unidirectional outline of the objects in the
original image. The regions which are constantly bright
become black, while other regions become highlighted. The
derivative can be applied in digital form in several ways. The
operator consists of a pair of 3 × 3 convolution kernels, which
are represented as follows:

Thus, when Sobel operator is applied, the resulted image is a
grayscale. Thus the result so obtained is (Figure 2):

Figure 2: Sobel operator-output image.

Roberts cross operator

The Roberts cross operator performs a quick 2D spatial
gradient measurement on an image. It is used to highlight the
regions of high spatial frequency which often seems to be the
edges detected in an image. The input to the operator often
tends to be a grayscale image, the output will also be a
grayscale image. Each pixel values at in the output represents
the calculable magnitude of the spatial gradient at that time.
The 2 × 2 convolution kernel given in Roberts cross operator
edge detection technique is as follows:

One kernel is just the other turned by 90º, this can be similar to
the Sobel operator. Thus the gradient magnitude is given by:

(4)

And the approximate magnitude which is faster to compute is
given by:

|G|=|Gx|+|Gy| (5)

The angle of the orientation of the edge is given by:

=arctan (Gy/Gx)-3/4 (6)

Example taken in this paper, Figure 3 is the input image.

Figure 3: Roberts operator-input image.

When Roberts cross operator is applied, the resulted image is a
grayscale image. Thus the result so obtained is (Figure 4):

Figure 4: Roberts operator-output image.

Prewitt operator

The Prewitt edge filter detects the edges by applying a
horizontal and vertical filter in sequence. Both filters are
applied to the image and then it is summed to get the final
result. This edge detector is one of the best ways to calculate
the magnitude and orientation that are found in an edge. It
doesn’t place any important role on the pixels that are found
closer to the center of the mask [6], unlike Sobel operator. This
operator is restricted to eight possible orientations, but most
direct orientation estimates don’t seem to be that much correct.
The Prewitt mask thus used is a 3 × 3 convolution kernel:

This operator is restricted to eight possible orientations, but
most direct orientation estimates don’t seem to be that much
correct. This edge detector is estimated in the 3 × 3
neighborhood for all eight directions. Then one convolution
mask is selected, which is of largest module [7]. Figure 5, the
image is taken as input image.

Figure 5: Prewitt operator-input image.

Thus when Prewitt operator is applied, the resulted image is a
grayscale image since the input given to act on Prewitt operator
is also a grayscale image. Thus the result so obtained is (Figure
6):

Figure 6: Prewitt operator-output image.

Canny operator

The canny edge detector is also well known to many as the
optimal detector, it aims to satisfy three main criteria:

1. Low error rate.

2. Good localization.

3. Minimal response.

There are some steps involved in canny edge detection
technique:

Filter out noise if any using the Gaussian filter.

Then find the intensity gradient of the image.

Then find the intensity gradient of the image.

Convolution kernel used in this technique is:

Then the magnitude and gradient strength are calculated.

|G|=|Gx|+|Gy|

θ=arctan (Gy/Gx) (8)

There are 4 possible angles (namely 0, 45, 90 or 135)

Non-maximum suppression is used to remove pixels that are
not found to be the part of an edge. Thus only thin lines
(candidate edges) will remain.

Hysteresis is the final step where canny uses two thresholds
namely: upper and lower

• Pixel is accepted as an edge if the pixel gradient value is
higher than the upper threshold.

• Pixel is rejected as an edge if the pixel gradient value is
below the lower threshold [8].

• If the value is between the two thresholds, then it will be
accepted only if it is connected to a pixel that tends to be
above the upper threshold [8].

• The ratio between upper and lower threshold is between 2:1
and 3:1.

Figure 7 is the image taken as input image.

Figure 7: Canny operator-input image.

Thus the result so obtained after applying canny edge detection
technique on the input image is (Figure 8):

Figure 8: LoG operator-input image.

Laplacian of Gaussian (Log) operator

Laplacian of a Gaussian operator is otherwise referred to as
LoG operator [5]. This method combines Gaussian filtering
with Laplacian for detection of an edge. It works by detecting
the zero crossings of the second derivative to find the edges in an image. Since the second derivative consists of noise, it has
to be removed before edge detection. Since the input image is
portrayed as a collection of distinct pixels, we've to seek out a
distinct convolution kernel which will approximate the second
derivatives within the definition of the Laplacian [1].

In the detection method of the Log operator, we firstly presmooth
the image with Gauss low-pass filter, and then find the
steep edge in the image using the Log operator. Finally, we
supply on banalization with zero gray level to present birth to
connected outline and eliminate all internal spots. However
double pixels boundary usually seems using the Log operator
to find an edge, and the operator is extremely sensitive to
noise. Therefore the Log operator is commonly used to
evaluate whether the edge pixels belongs to either bright
section or dark section of the image. Commonly used discrete
approximations are:

Since the convolution operation is associative, we will turn the
Gaussian smoothing filter with the Laplacian filter, first of all,
and then turn this hybrid filter with the image to attain the
desired result. Doing things this manner it has 2 advantages:

• Since both the Gaussian and also the Laplacian kernels are
sometimes much smaller than the image, this methodology
sometimes needs far fewer arithmetic operations.

• The LoG (‘Laplacian of Gaussian’) kernel may be precalculated
beforehand therefore only 1 convolution must be
performed at run-time on the image.

The watershed method is used to get the initial segmented
image [9]. Then it has been clustered. This approach has
reduced the unwanted region by providing the area under
suspicion.

Figure 9 is taken as an input image in this paper.

Figure 9: LoG operator-input image.

Thus the result so obtained after applying canny edge detection
technique on the input image is (Figure 10):

Figure 10: LoG operator-output image.

Conclusion

Thus, this paper allows us to get a clear idea about the edge
detection techniques used in the digital images. Whereas in
edge detection, various edge detecting operators are used on
the same input image of the bubble dosimeter image to find the
best technique. By visual perception, we can conclude clearly
that the Sobel, Prewitt, and Roberts give low-quality edge
maps relative to the others. A detailed illustration of the edges
in an image of this bubble detector dosimeter is obtained
through the canny and Laplacian of Gaussian ways. Among the
assorted methods investigated, the canny methodology is in a
position to find both strong and weak edges and appears to be
more appropriate than the Laplacian of Gaussian. A statistical
analysis of the performance offers a strong conclusion for this
complicated category of images. This analysis of edge
detection in the dosimeter bubble detector image will give good idea in the field of atomic to process the measure of the
neutron dosage in the radioactive premises. This image
processing based edge detector analysis can be applied in the
application of bio-medical field to count the number of cells in
the blood especially red blood cells and white blood cells.